Bayesian Integrative Analysis and Prediction with Application to\n Atherosclerosis Cardiovascular Disease
Bibliographic record
Abstract
Cardiovascular diseases (CVD), including atherosclerosis CVD (ASCVD), are\nmultifactorial diseases that present a major economic and social burden\nworldwide. Tremendous efforts have been made to understand traditional risk\nfactors for ASCVD, but these risk factors account for only about half of all\ncases of ASCVD. It remains a critical need to identify nontraditional risk\nfactors (e.g., genetic variants, genes) contributing to ASCVD. Further,\nincorporating functional knowledge in prediction models have the potential to\nreveal pathways associated with disease risk. We propose Bayesian hierarchical\nfactor analysis models that associate multiple omics data, predict a clinical\noutcome, allow for prior functional information, and can accommodate clinical\ncovariates. The models, motivated by available data and the need for other risk\nfactors of ASCVD, are used for the integrative analysis of clinical,\ndemographic, and multi-omics data to identify genetic variants, genes, and gene\npathways potentially contributing to 10-year ASCVD risk in healthy adults. Our\nfindings revealed several genetic variants, genes and gene pathways that were\nhighly associated with ASCVD risk. Interestingly, some of these have been\nimplicated in CVD risk. The others could be explored for their potential roles\nin CVD. Our findings underscore the merit in joint association and prediction\nmodels.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".